Application of machine learning to hydro-acoustic seismic and magmaticevents detections

Hydrophones arrays have proven to be an efficient and affordable method to monitor underwater soundscape, in particular magmatic and tectonic events. Indeed, thanks to the sound fixing and ranging (SOFAR) channel in the ocean, acoustic waves undergo a very low attenuation over distance and thus propagate further than they would do across the solid Earth. The MAHY array, composed of four autonomous hydrophones, has been deployed off Mayotte Island since October 2020. It contributes to monitor the recent volcanic activity around the island, and enabled to detect short and energetic acoustic events sometimes reffered to as impulsive events. As for their cause, it has been proposed that these signals are generated by water-lava interactions on the seafloor. So far, these events have been searched by visually inspecting the data, which is a cumbersome and somewhat observer-dependent task. To face these issues, we have developped an automatic picking algorithm tailored for these impulsive events. After some initial signal processing, a supervised neural network model was trained to detect such signals, which can be later checked by a human operator. Taking advantage of the genericity of this detection framework, we applied it to other hydroacoustic data sets (OHASISBIO and IMS-CTBT) to explore the feasibility of detecting T-wave generated by submarine earthquakes. The next step will be to improve the model with unsupervised or semi-supervised feature learning, in order to improve our metrics and, in the end, facilitate the study of specific acoustic signals.